U.S. patent number 7,505,806 [Application Number 10/626,320] was granted by the patent office on 2009-03-17 for fiber rendering apparatus.
This patent grant is currently assigned to GE Medical Systems Global Technology Company, LLC. Invention is credited to Osamu Abe, Shigeki Aoki, Hiroyuki Kabasawa, Yoshitaka Masutani.
United States Patent |
7,505,806 |
Masutani , et al. |
March 17, 2009 |
Fiber rendering apparatus
Abstract
For the purpose of preventing a situation in which the fiber
density looks as if it suddenly decreases in a specific view
direction, a method comprises: specifying a region of interest R1
in MR image data collected by a diffusion tensor method; defining
regular grid points in the region of interest R1; defining points
obtained by randomly moving the grid points as tracking start
points S1, S2, . . . ; performing diffusion tensor analysis on each
tracking start point S1, S2, . . . in the image data to determine a
direction of a principal axis vector; tracking a fiber by
repeatedly selecting a neighbor point along the direction of the
principal axis vector and performing diffusion tensor analysis on
the neighbor point to determine the direction of the principal axis
vector; and producing and displaying an image of the tracked fibers
as viewed in a desired view direction.
Inventors: |
Masutani; Yoshitaka (Tokyo,
JP), Abe; Osamu (Tokyo, JP), Aoki;
Shigeki (Tokyo, JP), Kabasawa; Hiroyuki (Tokyo,
JP) |
Assignee: |
GE Medical Systems Global
Technology Company, LLC (Waukesha, WI)
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Family
ID: |
32057407 |
Appl.
No.: |
10/626,320 |
Filed: |
July 24, 2003 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20050101857 A1 |
May 12, 2005 |
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Foreign Application Priority Data
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Aug 29, 2002 [JP] |
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2002-250628 |
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Current U.S.
Class: |
600/410; 324/307;
324/309; 382/128 |
Current CPC
Class: |
G01R
33/56341 (20130101); G06T 7/0012 (20130101); G06T
7/12 (20170101); G06T 7/181 (20170101); G06T
2207/10092 (20130101); G06T 2207/20021 (20130101); G06T
2207/20104 (20130101); G06T 2207/30016 (20130101) |
Current International
Class: |
A61B
5/05 (20060101); G01V 3/00 (20060101); G06K
9/00 (20060101) |
Field of
Search: |
;600/410 ;324/307,309
;382/128 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Aoki, et al., MRI On Central Nervous System, New Medicine in Japan,
Jun. 2002, pp. 72-75. cited by other.
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Primary Examiner: Winakur; Eric F
Assistant Examiner: Rozanski; Michael T
Attorney, Agent or Firm: Armstrong Teasdale LLP
Claims
The invention claimed is:
1. A fiber rendering apparatus comprising: a device for specifying
a region of interest or volumetric region of interest in
three-dimensional image data collected by a diffusion tensor method
in an MRJ apparatus; a device for defining regular grid points in
the region of interest or volumetric region of interest; a device
for defining points obtained by randomly moving the grid points
based on a distribution function in a two-dimensional or
three-dimensional manner as tracking start points; a device for
performing diffusion tensor analysis on each tracking start point
in the three-dimensional image data to determine a direction of a
principal axis vector; a device for tracking a fiber by repeatedly
selecting a neighbor point along the direction of the principal
axis vector and performing diffusion tensor analysis on the
neighbor point to determine a direction of a principal axis vector;
and a device for producing and displaying an image of the tracked
fibers as viewed in a desired view direction.
2. The fiber rendering apparatus of claim 1, wherein the image is
displayed with display colors based on the tracking start points
and neighbor points.
3. The fiber rendering apparatus of claim 1, wherein the image is
displayed with opacity based on the tracking start points and
neighbor points.
4. The fiber rendering apparatus of claim 1, wherein the tracked
fibers are white brain matter fibers.
5. The fiber rendering apparatus of claim 1, wherein the grid
points are randomly moved within a range such that, after moving,
the points fall within intervals between the original locations of
the grid points.
6. The fiber rendering apparatus of claim 1, wherein the grid
points are moved based at least one of a Gaussian function and a
uniform function.
7. The fiber rendering apparatus of claim 1, wherein the device for
tracking a fiber is capable of tracking the fiber when a fiber
density decreases in a three-dimensional volume.
8. A fiber rendering apparatus comprising: a device for defining
tracking start points in three-dimensional image data collected by
a diffusion tensor method in an MRI apparatus, wherein the tracking
start points are generated by randomly displacing a plurality of
grid points located in a region of interest based on a distribution
function; a device for performing diffusion tensor analysis on each
tracking start point in the three-dimensional image data to
determine a direction of a principal axis vector and a diffusion
anisotropy value; a device for tracking a fiber by repeatedly
selecting a neighbor point along the direction of the principal
axis vector and performing diffusion tensor analysis on the
neighbor point to determine a direction of a principal axis vector
and a diffusion anisotropy value; and a device for producing an
image of the tracked fibers as viewed in a desired view direction
and displaying the image with opacity reflecting the diffusion
anisotropy values at the tracking start points and neighbor
points.
9. The fiber rendering apparatus of claim 8, wherein an FA value is
used as the diffusion anisotropy value.
10. The fiber rendering apparatus of claim 9, wherein
X.sub.n+1=FA.sub.nX.sub.n, where X.sub.n+1 represents an opacity at
a neighbor point, FA.sub.n represents an FA value at the
immediately preceding neighbor point or tracking start point, and
X.sub.n represents an opacity thereat.
11. The fiber rendering apparatus of claim 8, wherein the tracked
fibers are white brain matter fibers.
12. The fiber rendering apparatus of claim 8, wherein the grid
points are randomly moved within a range such that, after moving,
the points fall within intervals between the original locations of
the grid points.
13. The fiber rendering apparatus of claim 8, wherein the grid
points are moved based at least one of a Gaussian function and a
uniform function.
14. The fiber rendering apparatus of claim 8, wherein the device
for tracking a fiber is capable of tracking the fiber when a fiber
density decreases in a three-dimensional volume.
15. A fiber rendering apparatus comprising: a device for defining
tracking start points in three-dimensional image data collected by
a diffusion tensor method in an MRI apparatus, wherein the tracking
start points are generated by randomly displacing a plurality of
grid points located in a region of interest based on a distribution
function; a device for performing diffusion tensor analysis on each
tracking start point in the three-dimensional image data to
determine a direction of a principal axis vector and eigenvalues of
a diffusion tensor; a device for tracking a fiber by repeatedly
selecting a neighbor point along the direction of the principal
axis vector and performing diffusion tensor analysis on the
neighbor point to determine a direction of a principal axis vector
and eigenvalues of a diffusion tensor; and a device for producing
an image of the tracked fibers as viewed in a desired view
direction and displaying the image with display colors reflecting
the eigenvalues of the diffusion tensors at the tracking start
points and neighbor points.
16. The fiber rendering apparatus of claim 15, wherein a display
color (R, G, B) is defined as:
R:G:B=1:.lamda.2/.lamda.1:.lamda.3/.lamda.1, where .lamda.1,
.lamda.2 and .lamda.3 represent eigenvalues of a diffusion
tensor.
17. The fiber rendering apparatus of claim 15, wherein the tracked
fibers are white brain matter fibers.
18. The fiber rendering apparatus of claim 15, wherein the grid
points are randomly moved within a range such that, after moving,
the points fall within intervals between the original locations of
the grid points.
19. The fiber rendering apparatus of claim 15, wherein the grid
points are moved based at least one of a Gaussian function and a
uniform function.
20. The fiber rendering apparatus of claim 15, wherein the device
for tracking a fiber is capable of tracking the fiber when a fiber
density decreases in a three-dimensional volume.
Description
BACKGROUND OF THE INVENTION
The present invention relates to a fiber rendering method and MRI
(magnetic resonance imaging) apparatus, and more particularly to a
method and MRI apparatus for properly rendering brain white matter
fibers obtained by diffusion tensor imaging.
FIG. 21 is a flow chart showing a conventional fiber rendering
method.
At Step P1, an MR image in an axial or oblique plane is produced
from three-dimensional image data collected by a diffusion tensor
method or another imaging method (T1- or T2-enhanced or the like)
in an MRI apparatus, and the MR image is displayed.
At Step P2, an operator specifies a two-dimensional region of
interest R1 (or a three-dimensional volumetric region of interest)
on a displayed MR image G1, as shown in FIG. 22.
At Step P3', regular grid points are generated in the region of
interest R1 (or in the volumetric region of interest) as shown in
FIG. 23, and they are defined as tracking start points S1, S2, S3,
. . . .
At Step P5, one of the tracking start points is selected.
At Step P6', diffusion tensor analysis is performed on the selected
tracking start point in the three-dimensional image data collected
by the diffusion tensor method in the MRI apparatus to determine
the direction of the principal axis vector, i.e., the direction of
the first eigenvector.
At Step P7, if a point at a unit distance along the direction of
the principal axis vector falls within the three-dimensional image
data space, the point is defined as a neighbor point and the flow
proceeds to Step P8'; and if the point falls outside the
three-dimensional image data space, the flow proceeds to Step
P11.
At Step P8', data at the neighbor point is created by interpolation
or the like on the three-dimensional image data, and diffusion
tensor analysis is performed to determine the direction of the
principal axis vector and the FA (fractional anisotropy) value.
At Step P9, if the FA value is equal to or more than a threshold,
the flow goes back to Step P7 to continue the fiber tracking
because the fiber tracking has not reached an end portion of a
brain white matter fiber; and if the FA value is less than the
threshold, the flow proceeds to Step P11 to terminate the fiber
tracking because an end portion of a brain white matter fiber has
been reached.
In this way, Steps P7-P9 are repeated until no more
three-dimensional image data are found or the fiber tracking has
reached an end portion of a brain white matter fiber, and a fiber
is tracked from the tracking start point S1 to a neighbor point N1,
N2, N3, . . . , as exemplarily shown in FIG. 24. At that time,
connectivity is decided by using a scalar product of vectors, for
example.
At Step P11, points from the tracking start point to the last
neighbor point are saved as one brain white matter fiber.
At Step P12, if any tracking start point not selected at Step P5
remains, the flow goes back to Step P5; otherwise, proceeds to Step
P14'.
At Step P14', an image of the saved brain white matter fibers as
viewed in a desired view direction is produced and displayed, as
exemplarily shown in FIG. 25.
A diffusion tensor and a nerve fiber extending direction are
described in, for example, "Microstructural and Physiological
Features of Tissues Elucidated by Ouantitative-Diffusion-Tensor
MRI" by Peter J. Basser and Carlo Pierpaoli, Journal of Magnetic
Resonance, Series B 111, pp. 209-219 (1996), and in "Diffusion
Anisotropy--2D and 3D images of Brain White Matter Fibers--" by
Yasuomi Kinosada (Kyoto Prefectural University of Medicine,
Department of Radiology), the 30 th Meeting of MR Imaging Study
Group, Sep. 4, 1998, at Sapporo, Japan.
SUMMARY OF THE INVENTION
When the grid points regularly generated at Step P3 in FIG. 21 are
defined as the tracking start points, the fiber density looks as if
it suddenly decreases when the view direction is parallel to a
direction of the grid point arrangement, because the nerve fibers
passing through the tracking start points lining up in the view
direction appear to overlap one another, leading to a problem that
the image gives an unnatural impression.
Thus, a first object of the present invention is to provide a fiber
rendering method capable of preventing a situation in which the
fiber density looks as if it suddenly decreases in a specific view
direction.
When the threshold at Step P9 in FIG. 21 is small, even a portion
having a considerably low FA value, i.e., a portion with
considerably low fiber tracking reliability, is rendered. The
portion with considerably low fiber tracking reliability is,
however, rendered in the same manner of display as that for
rendering a portion with high reliability, and these portions
cannot be distinguished, leading to the problem that this poses a
hindrance to accurate diagnosis. On the other hand, when the
threshold at Step P9 in FIG. 21 is large, the fiber tracking is
aborted before an end portion of a brain white matter fiber is
reached, leading to a problem that fibers cannot be fully
rendered.
Thus, a second object of the present invention is to provide a
fiber rendering method capable of rendering fibers in a manner of
display that incorporates the degree of fiber tracking
reliability.
In the conventional technique, since eigenvalues of diffusion
tensors are not incorporated in display of tracked fibers, there is
a problem that variation in eigenvalues of diffusion tensors cannot
be seen when observing the rendered fibers.
Thus, a third object of the present invention is to provide a fiber
rendering method capable of rendering fibers in a manner of display
that incorporates variation in the eigenvalues of diffusion
tensors.
As shown in FIG. 27, at a nerve fiber intersection C, nerve fibers
having different connection directions intersect each other. The
conventional tracking, however, employs only the direction of the
principal axis vector at a selected neighbor point, and therefore
it cannot distinguish between fibers that intersect each other at a
nerve fiber intersection, leading to a problem that the tracking
direction may be mistaken as shown in FIG. 28.
Thus, a fourth object of the present invention is to provide a
fiber rendering method capable of conducting tracking without
mistaking the direction even at a portion where nerve fibers having
different connection directions intersect each other.
In diagnosing leukodystrophy, for example, knowledge about whether
connection by fiber nerves between two sites has been destroyed
provides useful information.
Thus, a fifth object of the present invention is to provide a fiber
rendering method by which connectivity by fiber nerves between two
sites that an operator specifies can be visually recognized.
In accordance with its first aspect, the present invention provides
a fiber rendering method characterized in comprising: specifying a
region of interest or volumetric region of interest in
three-dimensional image data collected by a diffusion tensor method
in an MRI apparatus; defining regular grid points in the region of
interest or volumetric region of interest; then defining points
obtained by randomly moving the grid points in a two-dimensional or
three-dimensional manner as tracking start points; performing
diffusion tensor analysis on each tracking start point in the
three-dimensional image data to determine a direction of a
principal axis vector; tracking a fiber by repeatedly selecting a
neighbor point along the direction of the principal axis vector and
performing diffusion tensor analysis on the neighbor point to
determine a direction of a principal axis vector; and producing and
displaying an image of the tracked fibers as viewed in a desired
view direction.
In the fiber rendering method of the first aspect, the number of
tracking start points overlapping one another is approximately the
same in any view direction. Therefore, a situation in which the
fiber density looks as if it suddenly decreases in a specific view
direction is prevented. Taking an overall view of the region of
interest or volumetric region of interest, the density of the track
start points is uniform and no density variation occurs.
In accordance with its second aspect, the present invention
provides a fiber rendering method characterized in comprising:
defining tracking start points in three-dimensional image data
collected by a diffusion tensor method in an MRI apparatus;
performing diffusion tensor analysis on each tracking start point
in the three-dimensional image data to determine a direction of a
principal axis vector and a diffusion anisotropy value; tracking a
fiber by repeatedly selecting a neighbor point along the direction
of the principal axis vector and performing diffusion tensor
analysis on the neighbor point to determine a direction of a
principal axis vector and a diffusion anisotropy value; and
producing an image of the tracked fibers as viewed in a desired
view direction and displaying the image with opacity reflecting the
diffusion anisotropy values at the tracking start points and
neighbor points.
In the fiber rendering method of the second aspect, the
transparency of a fiber to be rendered is changed according to the
diffusion anisotropy value. Therefore, the degree of fiber tracking
reliability can be visually recognized from the transparency of the
rendered fibers.
In accordance with its third aspect, the present invention provides
the fiber rendering method having the aforementioned configuration,
characterized in that an FA value is used as the diffusion
anisotropy value.
In the fiber rendering method of the third aspect, the transparency
of a fiber to be rendered can be changed according to an FA value
that takes a value between zero and one depending upon the
diffusion anisotropy.
In accordance with its fourth aspect, the present invention
provides the fiber rendering method having the aforementioned
configuration, characterized in that: X.sub.n+1=FA.sub.nX.sub.n,
where X.sub.n+1 represents an opacity at a neighbor point, FA.sub.n
represents an FA value at the immediately preceding neighbor point
or tracking start point, and X.sub.n represents an opacity
thereat.
In the fiber rendering method of the fourth aspect, the
transparency can be gradually increased from the tracking start
point toward an end portion, and sharply increased at the end
portion.
In accordance with its fifth aspect, the present invention provides
a fiber rendering method characterized in comprising: defining
tracking start points in three-dimensional image data collected by
a diffusion tensor method in an MRI apparatus; performing diffusion
tensor analysis on each tracking start point in the
three-dimensional image data to determine a direction of a
principal axis vector and eigenvalues of a diffusion tensor;
tracking a fiber by repeatedly selecting a neighbor point along the
direction of the principal axis vector and performing diffusion
tensor analysis on the neighbor point to determine a direction of a
principal axis vector and eigenvalues of a diffusion tensor; and
producing an image of the tracked fibers as viewed in a desired
view direction and displaying the image with display colors
reflecting the eigenvalues of the diffusion tensors at the tracking
start points and neighbor points.
In the fiber rendering method of the fifth aspect, the display
color of fibers to be rendered is changed according to the
eigenvalues of the diffusion tensors. Therefore, the change in the
eigenvalues of the diffusion tensors can be visually recognized by
the change in the display color of the rendered fibers.
In accordance with its sixth aspect, the present invention provides
the fiber rendering method having the aforementioned configuration,
characterized in that: a display color (R, G, B) is defined as:
R:G:B=1:.lamda.2/.lamda.1:.lamda.3/.lamda.1, where .lamda.1,
.lamda.2 and .lamda.3 represent eigenvalues of a diffusion
tensor.
In the fiber rendering method of the sixth aspect, the diffusion
can be known to be more isotropic as the display color is closer to
white, and to be more anisotropic as the display color is closer to
red.
In accordance with its seventh aspect, the present invention
provides a fiber rendering method characterized in comprising:
defining tracking start points in three-dimensional image data
collected by a diffusion tensor method in an MRI apparatus;
performing diffusion tensor analysis on each tracking start point
in the three-dimensional image data to determine a direction of a
principal axis vector and defining the direction of the principal
axis vector as a tracking direction vector; tracking a fiber by
repeatedly selecting a neighbor point along the tracking direction
vector, performing diffusion tensor analysis on the neighbor point
to obtain diffusion tensor information, and determining a tracking
direction vector from the diffusion tensor information and at least
an immediately preceding tracking direction vector; and producing
and displaying an image of the tracked fibers as viewed in a
desired view direction.
In the fiber rendering method of the seventh aspect, since a new
tracking direction vector is determined from diffusion tensor
information of a neighbor point and at least an immediately
preceding tracking direction vector, nerve fibers in different
connection directions can be distinguished based on the preceding
connection directions even at a portion at which the nerve fibers
in different connection directions intersect each other, and the
nerve fibers can be tracked without mistaking the direction.
In accordance with its eighth aspect, the present invention
provides the fiber rendering method having the aforementioned
configuration, characterized in that:
d.sub.i+1={.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda.2(e.sub.2d.sub.i)e.sub.-
2+.lamda.3(e.sub.3d.sub.i)e.sub.3}/|.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda-
.2(e.sub.2d.sub.i)e.sub.2+.lamda.3(e.sub.3d.sub.i)e.sub.3|, where
.lamda.1, .lamda.2 and .lamda.3 represent eigenvalues of a
diffusion tensor at a neighbor point, e1, e2 and e3 represent
eigenvectors thereat, d.sub.i+1 represents a tracking direction
vector thereat, and d.sub.i represents a tracking direction vector
at an immediately preceding neighbor point or tracking start
point.
In the fiber rendering method of the eighth aspect, a tracking
direction vector d.sub.i+1 can be determined from an immediately
preceding tracking direction vector d.sub.i, and eigenvalues of a
diffusion tensor .lamda.1, .lamda.2 and .lamda.3 and eigenvectors
e1, e2 and e3 at a neighbor point.
In accordance with its ninth aspect, the present invention provides
a fiber rendering method characterized in comprising: specifying a
start region of interest and an end region of interest or a start
volumetric region of interest and an end volumetric region of
interest in three-dimensional image data collected by a diffusion
tensor method in an MRI apparatus; defining tracking start points
in the start region of interest or start volumetric region of
interest; tracking a fiber by performing diffusion tensor analysis
from each tracking start point in the three-dimensional image data;
deciding whether each tracked fiber passes through the end region
of interest or end volumetric region of interest; and producing and
displaying an image of only the fibers that are decided to pass
through as viewed in a desired view direction.
In the fiber rendering method of the ninth aspect, since only the
nerve fibers passing through two sites are rendered, connectivity
of the nerve fibers between the two sites can be visually
recognized.
In accordance with its tenth aspect, the present invention provides
the fiber rendering method having the aforementioned configuration,
characterized in comprising: calculating and displaying a total sum
with respect to all the fibers decided to pass through:
M_Value=.SIGMA..lamda.1FA/L, where .lamda.1 represents a first
eigenvalue of a diffusion tensor of a fiber decided to pass
through, FA represents an FA value thereof, and L represents the
total length of the fiber.
In the fiber rendering method of the tenth aspect, quantitative
assessment is enabled by employing M_Value as an indicator of the
strength of connection by nerve fibers between two sites.
In accordance with its eleventh aspect, the present invention
provides a fiber rendering apparatus characterized in comprising:
means for specifying a region of interest or volumetric region of
interest in three-dimensional image data collected by a diffusion
tensor method in an MRI apparatus; means for defining regular grid
points in the region of interest or volumetric region of interest;
means for defining points obtained by randomly moving the grid
points in a two-dimensional or three-dimensional manner as tracking
start points; means for performing diffusion tensor analysis on
each tracking start point in the three-dimensional image data to
determine a direction of a principal axis vector; means for
tracking a fiber by repeatedly selecting a neighbor point along the
direction of the principal axis vector and performing diffusion
tensor analysis on the neighbor point to determine a direction of a
principal axis vector; and means for producing and displaying an
image of the tracked fibers as viewed in a desired view
direction.
In the fiber rendering apparatus of the eleventh aspect, the fiber
rendering method of the first aspect can be suitably
implemented.
In accordance with its twelfth aspect, the present invention
provides a fiber rendering apparatus characterized in comprising:
means for defining tracking start points in three-dimensional image
data collected by a diffusion tensor method in an MRI apparatus;
means for performing diffusion tensor analysis on each tracking
start point in the three-dimensional image data to determine a
direction of a principal axis vector and a diffusion anisotropy
value; means for tracking a fiber by repeatedly selecting a
neighbor point along the direction of the principal axis vector and
performing diffusion tensor analysis on the neighbor point to
determine a direction of a principal axis vector and a diffusion
anisotropy value; and means for producing an image of the tracked
fibers as viewed in a desired view direction and displaying the
image with opacity reflecting the diffusion anisotropy values at
the tracking start points and neighbor points.
In the fiber rendering apparatus of the twelfth aspect, the fiber
rendering method of the second aspect can be suitably
implemented.
In accordance with its thirteenth aspect, the present invention
provides the fiber rendering apparatus having the aforementioned
configuration, characterized in that an FA value is used as the
diffusion anisotropy value.
In the fiber rendering apparatus of the thirteenth aspect, the
fiber rendering method of the third aspect can be suitably
implemented.
In accordance with its fourteenth aspect, the present invention
provides the fiber rendering apparatus having the aforementioned
configuration, characterized in that: X.sub.n+1=FA.sub.nX.sub.n,
where X.sub.n+1 represents an opacity at a neighbor point, FA.sub.n
represents an FA value at the immediately preceding neighbor point
or tracking start point, and X.sub.n represents an opacity
thereat.
In the fiber rendering apparatus of the fourteenth aspect, the
fiber rendering method of the fourth aspect can be suitably
implemented.
In accordance with its fifteenth aspect, the present invention
provides a fiber rendering apparatus characterized in comprising:
means for defining tracking start points in three-dimensional image
data collected by a diffusion tensor method in an MRI apparatus;
means for performing diffusion tensor analysis on each tracking
start point in the three-dimensional image data to determine a
direction of a principal axis vector and eigenvalues of a diffusion
tensor; means for tracking a fiber by repeatedly selecting a
neighbor point along the direction of the principal axis vector and
performing diffusion tensor analysis on the neighbor point to
determine a direction of a principal axis vector and eigenvalues of
a diffusion tensor; and means for producing an image of the tracked
fibers as viewed in a desired view direction and displaying the
image with display colors reflecting the eigenvalues of the
diffusion tensors at the tracking start points and neighbor
points.
In the fiber rendering apparatus of the fifteenth aspect, the fiber
rendering method of the fifth aspect can be suitably
implemented.
In accordance with its sixteenth aspect, the present invention
provides the fiber rendering apparatus having the aforementioned
configuration, characterized in that: a display color (R, G, B) is
defined as: R:G:B=1:.lamda.2/.lamda.1:.lamda.3/.lamda.1, where
.lamda.1, .lamda.2 and .lamda.3 represent eigenvalues of a
diffusion tensor.
In the fiber rendering apparatus of the sixteenth aspect, the fiber
rendering method of the sixth aspect can be suitably
implemented.
In accordance with its seventeenth aspect, the present invention
provides a fiber rendering apparatus characterized in comprising:
means for defining tracking start points in three-dimensional image
data collected by a diffusion tensor method in an MRI apparatus;
means for performing diffusion tensor analysis on each tracking
start point in the three-dimensional image data to determine a
direction of a principal axis vector and defining the direction of
the principal axis vector as a tracking direction vector; means for
tracking a fiber by repeatedly selecting a neighbor point along the
tracking direction vector, performing diffusion tensor analysis on
the neighbor point to obtain diffusion tensor information, and
determining a tracking direction vector from the diffusion tensor
information and at least an immediately preceding tracking
direction vector; and means for producing and displaying an image
of the tracked fibers as viewed in a desired view direction.
In the fiber rendering apparatus of the seventeenth aspect, the
fiber rendering method of the seventh aspect can be suitably
implemented.
In accordance with its eighteenth aspect, the present invention
provides the fiber rendering apparatus having the aforementioned
configuration, characterized in that:
d.sub.i+1={.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda.2(e.sub.2d.sub.i)e.sub.-
2+.lamda.3(e.sub.3d.sub.i)e.sub.3}/|.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda-
.2(e.sub.2d.sub.i)e.sub.2+.lamda.3(e.sub.3d.sub.i)e.sub.3|, where
.lamda.1, .lamda.2 and 3 represent eigenvalues of a diffusion
tensor at a neighbor point, e1, e2 and e3 represent eigenvectors
thereat, d.sub.i+1 represents a tracking direction vector thereat,
and d.sub.i represents a tracking direction vector at an
immediately preceding neighbor point or tracking start point.
In the fiber rendering apparatus of the eighteenth aspect, the
fiber rendering method of the eighth aspect can be suitably
implemented.
In accordance with its nineteenth aspect, the present invention
provides a fiber rendering apparatus characterized in comprising:
means for specifying a start region of interest and an end region
of interest or a start volumetric region of interest and an end
volumetric region of interest in three-dimensional image data
collected by a diffusion tensor method in an MRI apparatus; means
for defining tracking start points in the start region of interest
or start volumetric region of interest; means for tracking a fiber
by performing diffusion tensor analysis from each tracking start
point in the three-dimensional image data; means for deciding
whether each tracked fiber passes through the end region of
interest or end volumetric region of interest; and means for
producing and displaying an image of only the fibers that are
decided to pass through as viewed in a desired view direction.
In the fiber rendering apparatus of the nineteenth aspect, the
fiber rendering method of the ninth aspect can be suitably
implemented.
In accordance with its twentieth aspect, the present invention
provides the fiber rendering apparatus having the aforementioned
configuration, characterized in comprising: means for calculating
and displaying a total sum with respect to all the fibers decided
to intersect: M_Value=.SIGMA..lamda.1FA/L, where .lamda.1
represents a first eigenvalue of a diffusion tensor of a fiber
decided to pass through, FA represents an FA value thereof, and L
represents the total length of the fiber.
In the fiber rendering apparatus of the twentieth aspect, the fiber
rendering method of the tenth aspect can be suitably
implemented.
According to the fiber rendering method and fiber rendering
apparatus of the present invention, the following effects can be
obtained: (1) The number of tracking start points overlapping one
another is approximately the same in various view directions.
Therefore, a situation in which the fiber density looks as if it
suddenly decreases in a specific view direction is prevented.
Taking an overall view of the region of interest or volumetric
region of interest, the density of the track start points is
uniform and no density variation occurs; (2) A portion of a
rendered fiber having low transparency can be known to have high
fiber tracking reliability, and a portion having high transparency
can be known to have low fiber tracking reliability. Therefore,
even when a portion having considerably low fiber tracking
reliability is rendered, the portion with considerably low fiber
tracking reliability and the portion with high reliability can be
distinguished, which avoids hindrance to accurate diagnosis; (3)
Whether diffusion is isotropic or anisotropic can be visually
recognized from the display color of rendered fibers; (4) Nerve
fibers in different connection directions can be distinguished
based on the preceding connection directions even at a portion at
which the nerve fibers in different connection directions intersect
each other, and the nerve fibers can be tracked without mistaking
the direction; (5) Since only the nerve fibers passing through two
sites can be rendered, connectivity of the nerve fibers between the
two sites can be visually recognized; and (6) Quantitative
assessment on the strength of connection by nerve fibers between
two sites is enabled.
Further objects and advantages of the present invention will be
apparent from the following description of the preferred
embodiments of the invention as illustrated in the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram showing an MRI apparatus in accordance
with a first embodiment.
FIG. 2 is a flow chart showing fiber rendering processing in
accordance with the first embodiment.
FIG. 3 is a flow chart continued from FIG. 2.
FIG. 4 exemplarily shows a screen for specifying a region of
interest.
FIG. 5 exemplarily shows regularly arranged grid points.
FIG. 6 exemplarily shows irregularly position-shifted tracking
start points.
FIG. 7 is a conceptual diagram showing a fiber tracking
condition.
FIG. 8 exemplarily shows an image of obtained fibers as viewed in a
desired view direction.
FIG. 9 exemplarily shows an image of the obtained fibers as viewed
in another view direction.
FIG. 10 is a flow chart showing fiber rendering processing in
accordance with a second embodiment.
FIG. 11 is a flow chart continued from FIG. 10.
FIG. 12 is a flow chart continued from FIG. 11.
FIG. 13 is a conceptual diagram showing a tracking direction
vector.
FIG. 14 is a conceptual diagram showing that the tracking
directions are not mistaken even if fibers intersect each
other.
FIG. 15 is an explanatory diagram showing that the tracking
direction is not mistaken even at a nerve fiber intersection.
FIG. 16 is a flow chart showing fiber rendering processing in
accordance with a third embodiment.
FIG. 17 is a flow chart continued from FIG. 16.
FIG. 18 is a flow chart continued from FIG. 17.
FIG. 19 exemplarily shows a screen for specifying start and end
regions of interest.
FIG. 20 exemplarily shows a screen that displays only the fibers
connecting the start and end regions of interest.
FIG. 21 is a flow chart showing conventional fiber rendering
processing.
FIG. 22 exemplarily shows a screen for specifying a region of
interest.
FIG. 23 exemplarily shows regularly arranged tracking start
points.
FIG. 24 is a conceptual diagram showing a fiber tracking
condition.
FIG. 25 exemplarily shows an image of obtained fibers as viewed in
a desired view direction.
FIG. 26 exemplarily shows an image of the obtained fibers as viewed
in another view direction.
FIG. 27 is a conceptual diagram showing that fibers intersect each
other at a nerve fiber intersection.
FIG. 28 is an explanatory diagram showing that the tracking
direction is mistaken at the nerve fiber intersection.
DETAILED DESCRIPTION OF THE INVENTION
The present invention will now be described in more detail with
reference to embodiments shown in the accompanying drawings.
First Embodiment
FIG. 1 is a block diagram showing an MRI apparatus in accordance
with one embodiment of the present invention.
In the MRI apparatus 100, a magnet assembly 1 has a bore (cavity
portion) for inserting therein a subject, and is provided,
surrounding the bore, with a gradient coil (which comprises X-axis,
Y-axis and Z-axis coils, and the combination thereof determines
slice, warp and read axes) 1G for generating gradient magnetic
fields, a transmit coil 1T for applying RF pulses for exciting
spins of atomic nuclei within the subject, a receive coil 1R for
detecting NMR signals from the subject, and a static magnetic field
power supply 2 and static magnetic field coil 1C for generating a
static magnetic field.
It should be noted that permanent magnets may be employed in place
of the static magnetic field power supply 2 and static magnetic
field coil 1C (superconductive coil).
The gradient coil 1G is connected to a gradient coil driving
circuit 3. The transmit coil 1T is connected to an RF power
amplifier 4. The receive coil 1R is connected to a preamplifier
5.
A sequence memory circuit 8 operates the gradient coil driving
circuit 3 based on a stored pulse sequence in response to
instructions from a computer 7 to thereby generate gradient
magnetic fields from the gradient coil 1G. The sequence memory
circuit 8 also operates a gate modulation circuit 9 to modulate
high frequency output signals from an RF oscillation circuit 10
into pulsed signals of predefined timing and envelope. The pulsed
signals are applied to the RF power amplifier 4 as excitation
pulses, power-amplified in the RF power amplifier 4, and then
applied to the transmit coil 1T in the magnet assembly 1 to
transmit RF pulses.
The preamplifier 5 amplifies NMR signals from the subject detected
at the receive coil 1R in the magnet assembly 1, and inputs the
signals to a phase detector 12. The phase detector 12 phase-detects
the NMR signals from the preamplifier 5 employing the output from
the RF oscillation circuit 10 as a reference signal, and supplies
the phase-detected signals to an A/D converter 11. The A/D
converter 11 converts the phase-detected analog signals into MR
data in the form of digital signals, and inputs them to the
computer 7.
The computer 7 reads the MR data from the A/D converter 11, and
performs image reconstruction calculation to produce an MR image.
The computer 7 is also responsible for overall control such as
receiving information supplied from an operator console 13.
Furthermore, the computer 7 conducts fiber rendering processing,
which will be described later with reference to FIG. 2.
A display device 6 displays the MR image and a fiber image which
will be described later.
FIG. 2 is a flow chart showing fiber rendering processing by the
MRI apparatus 100.
At Step P1, an MR image in an axial or oblique plane is produced
from three-dimensional image data collected by a diffusion tensor
method or another imaging method (T1- or T2-enhanced or the like)
in the MRI apparatus 100, and the MR image is displayed.
At Step P2, an operator specifies a two-dimensional region of
interest R1 (or a three-dimensional volumetric region of interest)
on a displayed MR image G1, as shown in FIG. 4.
At Step P3, regular grid points g1, g2, g3, . . . are generated in
the region of interest R1 (or in the volumetric region of
interest), as shown in FIG. 5.
At Step P4, points obtained by randomly moving the grid points g1,
g2, g3, . . . in a two-dimensional (or three-dimensional) manner
are defined as tracking start points S1, S2, S3, . . . , as shown
in FIG. 6. Random numbers for the random moving can be generated
using a distribution function such as a Gaussian distribution or
uniform distribution. The range of the moving may be defined so
that most of the points after the moving fall within intervals
between the grid points g1, g2, g3, . . . .
At Step P5, one of the tracking start points is selected.
At Step P6, diffusion tensor analysis is performed on the selected
tracking start point in the three-dimensional image data collected
by the diffusion tensor method in the MRI apparatus 100 to
determine the direction of the principal axis vector, the FA value,
and the eigenvalues.
At Step P7, if a point at a unit distance along the direction of
the principal axis vector falls within the three-dimensional image
data space, the point is defined as a neighbor point and the flow
proceeds to Step P8; and if the point falls outside the
three-dimensional image data space, the flow proceeds to Step
P11.
At Step P8, data at the neighbor point is created by interpolation
or the like on the three-dimensional image data, and diffusion
tensor analysis is performed to determine the direction of the
principal axis vector, the FA value, and the eigenvalues.
At Step P9, if the FA value is equal to or more than a threshold,
the flow goes back to Step P7 to continue the fiber tracking
because the fiber tracking has not reached an end portion of a
brain white matter fiber; and if the FA value is less than the
threshold, the flow proceeds to Step P11 to terminate the fiber
tracking because an end portion of a brain white matter fiber has
been reached.
In this way, Steps P7-P9 are repeated until no more
three-dimensional image data are found or the fiber tracking has
reached an end portion of a brain white matter fiber, and a fiber
is tracked from the tracking start point S1 to a neighbor point N1,
N2, N3, . . . , as exemplarily shown in FIG. 7. At that time,
connectivity is decided by using a scalar product of vectors, for
example.
At Step P11, points from the tracking start point to the last
neighbor point are saved as one brain white matter fiber.
At Step P12, if any tracking start point not selected at Step P5
remains, the flow goes back to Step P5; otherwise, proceeds to Step
P14 in FIG. 3.
At Step P14 in FIG. 3, an image of the saved brain white matter
fibers as viewed in a desired view direction is produced, as
exemplarily shown in FIG. 8.
At Step P15, the opacity at the tracking start point is defined as
X.sub.0. Moreover, X.sub.n+1=FA.sub.nX.sub.n is set, where
X.sub.n+1 represents the opacity at a neighbor point, FA.sub.n
represents the FA value at the immediately preceding neighbor point
or tracking start point, and X.sub.n represents the opacity
thereat.
At Step P16, the display color (R, G, B) is defined as:
R:G:B=1:.lamda.2/.lamda.1:.lamda.3/.lamda.1, where .lamda.1,
.lamda.2 and .lamda.3 represent the eigenvalues of the diffusion
tensor.
At Step P17, the image of the fibers is displayed using the opacity
X and the display color (R, G, B).
According to the MRI apparatus 100 of the first embodiment, the
following effects can be obtained: (1) As shown in FIGS. 8 and 9,
the number of tracking start points overlapping one another is
approximately the same in various view directions. Therefore, a
situation in which the fiber density looks as if it suddenly
decreases in a specific view direction is prevented. Taking an
overall view of the region of interest or volumetric region of
interest, the density of the track start points is uniform and no
density variation occurs; (2) A portion of a rendered fiber having
low transparency can be known to have high fiber tracking
reliability, and a portion having high transparency can be known to
have low fiber tracking reliability. Therefore, even when a portion
having considerably low fiber tracking reliability is rendered by
decreasing the threshold at Step P9 in FIG. 2, the portion with
considerably low fiber tracking reliability and the portion with
high reliability can be distinguished, which avoids hindrance to
accurate diagnosis; and (3) The diffusion can be known as being
more isotropic as the display color for the rendered fibers is
closer to white, and as being more anisotropic as the display color
is closer to red.
In addition, modifications as follows may be made: (1) The opacity
X may be calculated based on another indicator that reflects the
diffusion anisotropy (for example, the eigenvalue ratio,
.lamda.2/.lamda.1, .lamda.3/.lamda.1, relative anisotropy, volume
ratio); and (2) The display color (R, G, B) may be determined as
R:G:B=.lamda.1/(.lamda.1+.lamda.2+3):.lamda.2/(.lamda.1.lamda.2+3):.lamda-
.3/(.lamda.1+.lamda.2+.lamda.3).
Second Embodiment
FIG. 10 is a flow chart showing fiber rendering processing by an
MRI apparatus in accordance with a second embodiment.
At Step Q1, an MR image in an axial or oblique plane is produced
from three-dimensional image data collected by a diffusion tensor
method or another imaging method (T1- or T2-enhanced or the like)
in the MRI apparatus, and the MR image is displayed.
At Step Q2, an operator specifies a two-dimensional region of
interest R1 (or a three-dimensional volumetric region of interest)
on a displayed MR image G1, as shown in FIG. 4.
At Step Q3, regular grid points g1, g2, g3, . . . are generated in
the region of interest R1 (or in the volumetric region of
interest), as shown in FIG. 5.
At Step Q4, points obtained by randomly moving the grid points g1,
g2, g3, . . . in a two-dimensional (or three-dimensional) manner
are defined as tracking start points S1, S2, S3, . . . , as shown
in FIG. 6. Random numbers for the random moving can be generated
using a distribution function such as a Gaussian distribution or
uniform distribution. The flow then proceeds to Step Q5 in FIG.
11.
At Step Q5 in FIG. 11, one of the tracking start points is
selected.
At Step Q6, diffusion tensor analysis is performed on the selected
tracking start point in the three-dimensional image data collected
by the diffusion tensor method in the MRI apparatus to determine
the direction of the principal axis vector, the FA value, and the
eigenvalues, and the principal axis vector is defined as a tracking
direction vector.
At Step Q7, if three-dimensional image data corresponding to a
point at a unit distance along the direction of the tracking
direction vector is present, the point is defined as a neighbor
point and the flow proceeds to Step Q8; and if no three-dimensional
image data corresponding to a point at a unit distance along the
direction of the principal axis vector is present, the flow
proceeds to Step Q11.
At Step Q8, data at the neighbor point is created by interpolation
or the like on the three-dimensional image data, and diffusion
tensor analysis is performed to determine the eigenvectors, FA
value, and eigenvalues.
At Step Q9, if the FA value is equal to or more than a threshold,
the flow proceeds to Step Q10 to continue the fiber tracking
because the fiber tracking has not reached an end portion of a
brain white matter fiber; and if the FA value is less than the
threshold, the flow proceeds to Step Q11 to terminate the fiber
tracking because an end portion of a brain white matter fiber has
been reached.
At Step Q10,
d.sub.i+1={.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda.2(e.sub.2d.sub.i)e.sub.-
2+.lamda.3(e.sub.3d.sub.i)e.sub.3}/|.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda-
.2(e.sub.2d.sub.i)e.sub.2+.lamda.3(e.sub.3d.sub.i)e.sub.3| is set,
where .lamda.1, .lamda.2 and .lamda.3 represent the eigenvalues of
the diffusion tensor at a neighbor point, e1, e2 and e3 represent
the eigenvectors thereat, d.sub.i+1 represents the tracking
direction vector thereat, and d.sub.i represents the tracking
direction vector at the immediately preceding neighbor point or
tracking start point.
FIG. 13 is a conceptual diagram showing the tracking direction
vector d.sub.i+1.
The flow then goes back to Step Q7.
In this way, Steps Q7-Q10 are repeated until no more
three-dimensional image data are found or the fiber tracking has
reached an end portion of a brain white matter fiber, and a fiber
is tracked from the tracking start point S1 to a neighbor point N1,
N2, N3, . . . , as exemplarily shown in FIG. 7. At that time,
connectivity is decided by using a scalar product of vectors, for
example.
At Step Q11, points from the tracking start point to the last
neighbor point are saved as one brain white matter fiber.
At Step Q12, if any tracking start point not selected at Step Q5
remains, the flow goes back to Step Q5; otherwise, proceeds to Step
Q14 in FIG. 12.
At Step Q14 in FIG. 12, an image of the saved brain white matter
fibers as viewed in a desired view direction is produced, as
exemplarily shown in FIG. 8.
At Step Q15, the opacity at the tracking start point is defined as
X.sub.0. Moreover, X.sub.n+1=FA.sub.nX.sub.n is set, where
X.sub.n+1 represents the opacity at a neighbor point, FA.sub.n
represents the FA value at the immediately preceding neighbor point
or tracking start point, and X.sub.n represents the opacity
thereat.
At Step Q16, the display color (R, G, B) is defined as:
R:G:B=1:.lamda.2/.lamda.1:.lamda.3/.lamda.1, where .lamda.1,
.lamda.2 and .lamda.3 represent the eigenvalues of the diffusion
tensor.
At Step Q17, the image of the fibers is displayed using the opacity
X and the display color (R, G, B).
According to the MRI apparatus of the second embodiment, the
following effect can be obtained in addition to those in the first
embodiment: (4) As shown in FIG. 14, if the immediately preceding
tracking direction vectors d.sub.i and d.sub.j are different, the
tracking direction vectors d.sub.i+1 and d.sub.j+1 will be
different even if the neighbor points N.sub.i+1 and N.sub.j+1
coincide with or lie close to each other. Therefore, nerve fibers
in different connection directions can be distinguished based on
the preceding connection directions even at a nerve fiber
intersection C at which the nerve fibers in different connection
directions intersect each other, as shown in FIG. 15, and the nerve
fibers can be tracked without mistaking the direction.
In addition, to determine the tracking direction vector, an
appropriate number N may be given to take an average vector of the
next through N-th preceding tracking direction vectors.
Third Embodiment
FIG. 16 is a flow chart showing fiber rendering processing by an
MRI apparatus in accordance with a third embodiment.
At Step Q1, an MR image in an axial or oblique plane is produced
from three-dimensional image data collected by a diffusion tensor
method or another imaging method (T1- or T2-enhanced or the like)
in the MRI apparatus, and the MR image is displayed.
At Step Q2', an operator specifies a two-dimensional start region
of interest R1 (or a three-dimensional start volumetric region of
interest) and a two-dimensional end region of interest R2 (or a
three-dimensional end volumetric region of interest) on a displayed
MR image G1, as shown in FIG. 19.
At Step Q3, regular grid points g1, g2, g3, . . . are generated in
the start region of interest R1 (or in the start volumetric region
of interest), as shown in FIG. 5.
At Step Q4, points obtained by randomly moving the grid points g1,
g2, g3, . . . in a two-dimensional (or three-dimensional) manner
are defined as tracking start points S1, S2, S3, . . . , as shown
in FIG. 6. Random numbers for the random moving can be generated
using a distribution function such as a Gaussian distribution or
uniform distribution. The flow then proceeds to Step Q5 in FIG.
17.
At Step Q5 in FIG. 17, one of the tracking start points is
selected.
At Step Q6, diffusion tensor analysis is performed on the selected
tracking start point in the three-dimensional image data collected
by the diffusion tensor method in the MRI apparatus to determine
the direction of the principal axis vector, the FA value, and the
eigenvalues, and the principal axis vector is defined as a tracking
direction vector.
At Step Q7, if a point at a unit distance along the direction of
the tracking direction vector falls within the three-dimensional
image data space, the point is defined as a neighbor point and the
flow proceeds to Step Q8; and if the point falls outside the
three-dimensional image data space, the flow proceeds to Step
Q11.
At Step Q8, data at the neighbor point is created by interpolation
or the like on the three-dimensional image data, and diffusion
tensor analysis is performed to determine the eigenvectors, FA
value, and eigenvalues.
At Step Q9, if the FA value is equal to or more than a threshold,
the flow proceeds to Step Q10 to continue the fiber tracking
because the fiber tracking has not reached an end portion of a
brain white matter fiber; and if the FA value is less than the
threshold, the flow proceeds to Step Q11 to terminate the fiber
tracking because an end portion of a brain white matter fiber has
been reached.
At Step Q10,
d.sub.i+1={.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda.2(e.sub.2d.sub.i)e.sub.-
2+.lamda.3(e.sub.3d.sub.i)e.sub.3}/|.lamda.1(e.sub.1d.sub.i)e.sub.1+.lamda-
.2(e.sub.2d.sub.i)e.sub.2+.lamda.3(e.sub.3d.sub.i)e.sub.3| is set,
where .lamda.1, .lamda.2 and .lamda.3 represent the eigenvalues of
the diffusion tensor at a neighbor point, e1, e2 and e3 represent
the eigenvectors thereat, d.sub.i+1 represents tracking direction
vector thereat, and d.sub.i represents the tracking direction
vector at the immediately preceding neighbor point or tracking
start point.
FIG. 13 is a conceptual diagram showing the tracking direction
vector d.sub.i+1.
The flow then goes back to Step Q7.
In this way, Steps Q7-Q10 are repeated until no more
three-dimensional image data are found or the fiber tracking has
reached an end portion of a brain white matter fiber, and a fiber
is tracked from the tracking start point S1 to a neighbor point N1,
N2, N3, . . . , as exemplarily shown in FIG. 7. At that time,
connectivity is decided by using a scalar product of vectors, for
example.
At Step Q11, points from the tracking start point to the last
neighbor point are saved as one brain white matter fiber.
At Step Q12, if any tracking start point that not selected at Step
Q5 remains, the flow goes back to Step Q5; otherwise, proceeds to
Step Q13 in FIG. 18.
At Step Q13 in FIG. 18, a decision is made as to whether the
obtained fiber has an intersection with the end region of interest
R2 (or the end volumetric region of interest), and the fiber is
selected only if it has an intersection.
At Step Q14', an image of only the selected brain white matter
fibers f as viewed in a desired view direction is produced, as
exemplarily shown in FIG. 20.
At Step Q15, the opacity at the tracking start point is defined as
X.sub.0. Moreover, X.sub.n+1=FA.sub.nX.sub.n is set, where
X.sub.n+1 represents the opacity at a neighbor point, FA.sub.n
represents the FA value at the immediately preceding neighbor point
or tracking start point, and X.sub.n represents the opacity
thereat.
At Step Q16, the display color (R, G, B) is defined as:
R:G:B=1:.lamda.2/.lamda.1:.lamda.3/.lamda.1, where .lamda.1,
.lamda.2 and .lamda.3 represent the eigenvalues of the diffusion
tensor.
At Step Q17, the image of the selected fibers is displayed using
the opacity X and the display color (R, G, B).
At Step Q18, a total sum with respect to all the selected fibers:
M_Value=.SIGMA..lamda.1FA/L is calculated and displayed, where
.lamda.1 represents the first eigenvalue of the diffusion tensor of
the selected fiber, FA represents the FA value thereof, and L
represents the total length of the fiber.
According to the MRI apparatus of the third embodiment, the
following effects can be obtained in addition to those in the
second embodiment: (5) Since only the nerve fibers f passing
through two sites are rendered, connectivity of the nerve fibers
between the two sites can be visually recognized; and (6)
Quantitative assessment is enabled by employing M_Value as an
indicator of the strength of connection by nerve fibers between two
sites.
It is possible to display an average M_Value by dividing M_Value by
the number of selected fibers.
Moreover, the fibers may be displayed with the display brightness
or display color changed according to M_Value.
Many widely different embodiments of the invention may be
configured without departing from the spirit and the scope of the
present invention. It should be understood that the present
invention is not limited to the specific embodiments described in
the specification, except as defined in the appended claims.
* * * * *